Recommender Systems Clustering Using Bayesian Non Negative Matrix Factorization

Bobadilla, Jesús; Bojorque, Rodolfo; Esteban, Antonio Hernando; Hurtado, Remigio

doi:10.1109/access.2017.2788138

Cited by 73 publications

(53 citation statements)

References 54 publications

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“…Moreover, the probabilistic view gives wider application. Indeed, Bayesian NMF [41,12] has been applied to image recognition [12], audio signal processing [41], overlap community detection [31], and recommender systems [10]. From a statistical point of view, the data matrices are random variables subject to the true distribution.…”

Section: Stochastic Matrix Factorizationmentioning

confidence: 99%

Asymptotic Bayesian Generalization Error in Latent Dirichlet Allocation and Stochastic Matrix Factorization

Hayashi¹,

Watanabe²

2020

SN COMPUT. SCI.

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We analyze the asymptotic behavior of the Bayesian generalization error in the topic model. Through a theoretical analysis of the maximum pole of the zeta function (real log canonical threshold) of the topic model, we obtain an upper bound of the Bayesian generalization error and the free energy in the topic model and stochastic matrix factorization (SMF; it can be regarded as a restriction of the non-negative matrix factorization). We show that the generalization error in the topic model and SMF becomes smaller than that of regular statistical models if Bayesian inference is attained.

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Section: Stochastic Matrix Factorizationmentioning

confidence: 99%

Asymptotic Bayesian Generalization Error in Latent Dirichlet Allocation and Stochastic Matrix Factorization

Hayashi¹,

Watanabe²

2020

SN COMPUT. SCI.

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“…CF RS are based on the preferences of users about items; preferences can be explicit (votes) or implicit (listened songs, purchased items, watched movies, etc.). CF RS have been traditionally implemented by using the K-Nearest Neighbors (KNN) machine learning method [12], although current CF RS kernels are usually based on the Matrix Factorization (MF) algorithm [13], [14]. MF converts the sparse matrix of ratings (users x items) to two dense matrices: 'users x factors' and 'factors x items'.…”

Section: A Recommendations To Individual Usersmentioning

confidence: 99%

“…Preclustering is a very dependent task on the data scope. RS clustering can be particularly improved by using power users [13], [39], and then we make this process. The algorithm KMeansPlusLogPower chooses K users from the dataset, as centroids.…”

Section: The Second Block Inmentioning

confidence: 99%

A Collaborative Filtering Probabilistic Approach for Recommendation to Large Homogeneous and Automatically Detected Groups

Bobadilla¹,

Gutiérrez²,

Alonso³

et al. 2020

IJIMAI

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In the collaborative filtering recommender systems (CFRS) field, recommendation to group of users is mainly focused on stablished, occasional or random groups. These groups have a little number of users: relatives, friends, colleagues, etc. Our proposal deals with large numbers of automatically detected groups. Marketing and electronic commerce are typical targets of large homogenous groups. Large groups present a major difficulty in terms of automatically achieving homogeneity, equilibrated size and accurate recommendations. We provide a method that combines diverse machine learning algorithms in an original way: homogeneous groups are detected by means of a clustering based on hidden factors instead of ratings. Predictions are made using a virtual user model, and virtual users are obtained by performing a hidden factors aggregation. Additionally, this paper selects the most appropriate dimensionality reduction for the explained RS aim. We conduct a set of experiments to catch the maximum cumulative deviation of the ratings information. Results show an improvement on recommendations made to large homogeneous groups. It is also shown the desirability of designing specific methods and algorithms to deal with automatically detected groups.

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“…Jesús Bobadilla et el. [33]recommended a Bayesian NMF (BNMF) method for the improvement in the current clustering output in collaborative based recommendation. The execution time of this proposed BNMF algorithm is better as compared to other classical Matrix Factorization models.…”

Section: B Latent Factor Modelmentioning

confidence: 99%

A Research on Collaborative Filtering Based Movie Recommendations: From Neighborhood to Deep Learning Based System

Behera*,

Das,

Swetanisha

2019

IJRTE

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Recommender System or Recommendation Engine gaining popularity as it can tackle information overload problem. Initially it was considered as a domain of Information Retrieval system and was limited to few applications. With the advancement of different state-of-the-art modeling approaches recommender system can be applicable to many application domains. Movie Recommender System (MRS) is widely explored domain and used by many streaming service providers like Netflix, Amazon Prime, YouTube and many more. This system makes use of users’ data to explore and recommends personally as per their taste. In this paper a detailed study on recently published article related to movie recommendation is carried out. Popular techniques for MRS are commonlycategorized into collaborative filtering, content-based and hybridmethod. Neighborhood-based, latent factor based, neural network based and deep learning based techniques have been continuously evolved with application to MRS. Recently proposed models have been reviewed and it is found that hybrid method performs better as compared to individual model.

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Recommender Systems Clustering Using Bayesian Non Negative Matrix Factorization

Cited by 73 publications

References 54 publications

Asymptotic Bayesian Generalization Error in Latent Dirichlet Allocation and Stochastic Matrix Factorization

Asymptotic Bayesian Generalization Error in Latent Dirichlet Allocation and Stochastic Matrix Factorization

A Collaborative Filtering Probabilistic Approach for Recommendation to Large Homogeneous and Automatically Detected Groups

A Research on Collaborative Filtering Based Movie Recommendations: From Neighborhood to Deep Learning Based System

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